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We investigate the circulation of nano- and micro-particles, including spherical particles and filamentous nanoworms, with red blood cells (RBCs) suspension in a constricted channel that mimics a stenosed microvessel. Through three-dimensional simulations using the immersed boundary-based Lattice Boltzmann method, the influence of channel geometries, such as the length and ratio of the constriction, on the accumulation of particles is systematically studied. Firstly, we find that the accumulation of spherical particles with 1 μm diameter in the constriction increases with the increases of both the length and ratio of the constriction. This is attributed to the interaction between spheres and RBCs. The RBCs “carry” the spheres and they accumulate inside the constriction together, due to the altered local hydrodynamics induced by the existence of the constriction. Secondly, nanoworms demonstrate higher accumulation than that of spheres inside the constriction, which is associated with the escape of nanoworms from RBC clusters and their accumulation near the wall of main channel. The accumulated near-wall nanoworms will eventually enter the constriction, thus enhancing their concentration inside the constriction. However, an exceptional case occurs in the case of constrictions with large ratio and long length. In such circumstances, the RBCs aggregate together tightly and concentrate at the center of the channel, which makes the nanoworms hardly able to escape from RBC clusters, leading to a similar accumulation of nanoworms and spheres inside the constriction. This study may provide theoretical guidance for the design of nano- and micro-particles for biomedical engineering applications, such as drug delivery systems for patients with stenosed microvessels. 

Magnetic actuation has emerged as a powerful and versatile mechanism for diverse applications, ranging from soft robotics, biomedical devices to functional metamaterials. This highly interdisciplinary research calls for an easy to use and efficient modeling/simulation platform that can be leveraged by researchers with different backgrounds. Here we present a lattice model for hard-magnetic soft materials by partitioning the elastic deformation energy into lattice stretching and volumetric change, so-called ‘magttice’. Magnetic actuation is realized through prescribed nodal forces in magttice. We further implement the model into the framework of a large-scale atomic/molecular massively parallel simulator (LAMMPS) for highly efficient parallel simulations. The magttice is first validated by examining the deformation of ferromagnetic beam structures, and then applied to various smart structures, such as origami plates and magnetic robots. After investigating the static deformation and dynamic motion of a soft robot, the swimming of the magnetic robot in water, like jellyfish's locomotion, is further studied by coupling the magttice and lattice Boltzmann method (LBM). These examples indicate that the proposed magttice model can enable more efficient mechanical modeling and simulation for the rational design of magnetically driven smart structures. 

Building upon our previous studies on interactions of amphiphilic Janus nanoparticles with glass-supported lipid bilayers, we study here how these Janus nanoparticles perturb the structural integrity and induce shape instabilities of membranes of giant unilamellar vesicles (GUVs). We show that 100 nm amphiphilic Janus nanoparticles disrupt GUV membranes at a threshold particle concentration similar to that in supported lipid bilayers, but cause drastically different membrane deformations, including membrane wrinkling, protrusion, poration, and even collapse of entire vesicles. By combining experiments with molecular simulations, we reveal how Janus nanoparticles alter local membrane curvature and collectively compress the membrane to induce shape transformation of vesicles. Our study demonstrates that amphiphilic Janus nanoparticles disrupt vesicle membranes differently and more effectively than uniform amphiphilic particles. 

Study of the permeability of small organic molecules across lipid membranes plays a significant role in designing potential drugs in the field of drug discovery. Approaches to design promising drug molecules have gone through many stages, from experiment-based trail-and-error approaches, to the well-established avenue of the quantitative structure–activity relationship, and currently to the stage guided by machine learning (ML) and artificial intelligence techniques. In this work, we present a study of the permeability of small drug-like molecules across lipid membranes by two types of ML models, namely the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models. Molecular descriptors and fingerprints are used for featurization of organic molecules. Using molecular descriptors, the LASSO model uncovers that the electro-topological, electrostatic, polarizability, and hydrophobicity/hydrophilicity properties are the most important physical properties to determine the membrane permeability of small drug-like molecules. Additionally, with molecular fingerprints, the LASSO model suggests that certain chemical substructures can significantly affect the permeability of organic molecules, which closely connects to the identified main physical properties. Moreover, the DNN model using molecular fingerprints can help develop a more accurate mapping between molecular structures and their membrane permeability than LASSO models. Our results provide deep understanding of drug–membrane interactions and useful guidance for the inverse molecular design of drug-like molecules. Last but not least, while the current focus is on the permeability of drug-like molecules, the methodology of this work is general and can be applied for other complex physical chemistry problems to gain molecular insights.